MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
arxiv(2024)
摘要
Multimodal emotion recognition is an important research topic in artificial
intelligence. Over the past few decades, researchers have made remarkable
progress by increasing dataset size and building more effective architectures.
However, due to various reasons (such as complex environments and inaccurate
labels), current systems still cannot meet the demands of practical
applications. Therefore, we plan to organize a series of challenges around
emotion recognition to further promote the development of this field. Last
year, we launched MER2023, focusing on three topics: multi-label learning,
noise robustness, and semi-supervised learning. This year, we continue to
organize MER2024. In addition to expanding the dataset size, we introduce a new
track around open-vocabulary emotion recognition. The main consideration for
this track is that existing datasets often fix the label space and use majority
voting to enhance annotator consistency, but this process may limit the model's
ability to describe subtle emotions. In this track, we encourage participants
to generate any number of labels in any category, aiming to describe the
character's emotional state as accurately as possible. Our baseline is based on
MERTools and the code is available at:
https://github.com/zeroQiaoba/MERTools/tree/master/MER2024.
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